Carta Geomorfologica del bacino idrografico del Rio Spinasanta e note illustrative(Sicilia centro-settentrionale).

Geomorphological map of the Rio Spinasanta river basin and illustrative notes (Central-northern Sicily). The present paper aims to illustrate the geomorphological map of the Rio Spinasanta river basin, in which landforms recognizable on the area are mapped and distinguished according to the responsible geomorphological process. The Rio Spianasanta river is a tributary of the head sector of the Imera Settentrionale river and is contiguous to the regional water divide that separate the northward and southward flowing Sicilian rivers; the geomorphological map has been produced using as support a topographic map on scale 1:10,000. The geomorphological map has been carried out by means of different methodologies, namely geological and geomorphological field surveys, analysis of aerial photos and orthophotos.Moreover, the geomorphological characterization of the area has been supported by the study of the pluviometric conditions and by the analysis of the landforms’ spatial distribution; the latter, which has been carried out using a GIS software, allowed to evaluate the density of landforms on the classes of the lithology parameter and on the classes of slope angle and aspect. The GIS analysis showed that the combinations of the selected parameters control the intensity of the processes and the spatial distribution of the shaped landforms; this fact led to different landscapes recognizable in the studied area. The landforms mapped in the Rio Spinasanta river basin have been distinguished according to the modeling processes in: a) landforms shaped by water erosion processes; b) landforms produced by gravitational processes

UTILIZZO DEL SISTEMA GOOGLE EARTH PER LA DEFINIZIONE DI UN MODELLO DI SUSCETTIBILITÀ DA FRANA: UN TEST IN SICILIA CENTRALE

Exploiting Google EarthTM to assess a landslide susceptibility model: a test in central Sicily. A landslide susceptibility multivariate model, based on the conditional analysis approach, has been derived in the Tumarrano river basin (about 78 km2), by intersecting a GIS grid layer, expressing some selected geo-environmental conditions (outcropping lithology, steepness, plan curvature and topographic wetness index), and a landslide vector archive, produced by a Google EarthTM aided remote survey. The analysis of the Google EarthTM images dated at 2006, allowed to recognize 733 landslides (30 rotational slides and 703 flows), almost exclusively affecting clay and sandy clay rocks. Validation procedures produced largely satisfactory results, which were analyzed in the domain of the success and prediction rate curves. The research confirms the goodness of the susceptibility assessment method, as well as the powerful of Google EarthTM as a tool to manage the need of new, detailed and multi-temporal landslide archives

Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy)

Forward logistic regression has allowed us to derive an earth-flow susceptibility model for the Tumarrano river basin, which was defined by modeling the statistical relationships between an archive of 760 events and a set of 20 predictors. For each landslide in the inventory, a landslide identification point (LIP) was automatically produced as corresponding to the highest point along the boundary of the landslide polygons, and unstable conditions were assigned to cells at a distance up to 8m. An equal number of stable cells (out of landslides) was then randomly extracted and appended to the LIPs to prepare the dataset for logistic regression. A model building strategy was applied to enlarge the area included in training the model and to verify the sensitivity of the regressed models with respect to the locations of the selected stable cells. A suite of 16 models was prepared by randomly extracting different unoverlapping stable cell subsets that have been appended to the unstable ones. Models were finally submitted to forward logistic regression and validated. The results showed satisfying and stable error rates (0.236 on average, with a standard deviation of 0.007) and areas under the receiver operating characteristic (ROC) curve (AUCs) (0.839 for training and 0.817 for test datasets) as well as factor selections (ranks and coefficients). As regards the predictors, steepness and large-profile and local-plan topographic curvatures were systematically selected. Clayey outcropping lithology, midslope drainage, local and midslope ridges, and canyon landforms were also very frequently (from eight to 15 times) included in the models by the forward selection procedures. The model-building strategy allowed us to produce a performing earth-flow susceptibility model, whose model fitting, prediction skill, and robustness were estimated on the basis of validation procedures, demonstrating the independence of the regressed model on the specific selection of the stable cells

Morphometric and hydraulic geometry assessment of a gully in SW Spain

Gully erosion represents one of the most significant types of land degradation in the Mediterranean areas, giving place to important on- and off-site effects. In this paper, a second-order gully located in SW Spain is analyzed. Along the gully, 28 cross-sections were established and measured with a Leica TCRM1102 laser total station, approximately every 6 months from 2001 to 2007. The sections were located at variable distance, placing them in areas where active erosion was evident. In total, 13 field measurements were carried out, and the geometric characteristics of 28 cross-sections were obtained. Morphometric analyses were carried out in both the main gully and a tributary reach by applying an empirical relationship between channel length and eroded volume. Morphometric variables of the gully sections were combined into two dimensionless groups, and a morphological similarity between different linear erosion landforms (rills, ephemeral and permanent gullies) was obtained. Then, the coefficient of variation of the calculated volumes was used to compare the instability between the main gully and the tributary reach. Finally, the hydraulic geometry of the gully was assessed by calibrating three empirical power equations, which relate bankfull discharge with mean flow velocity, cross-sectional depth and width. The hydraulic characterization of the main gully and the tributary reach was investigated for each field survey and a different behavior was detected. The hydraulic analysis also demonstrated that higher values of discharge provide better predictions of flow velocity; the size of the main and tributary gullies affects the discharge–width relationship; and that gully depth is the variable which can be predicted with the highest accuracy

Evaluation of debris flow susceptibility in El Salvador (CA): a comparison between Multivariate Adaptive Regression Splines (MARS) and Binary Logistic Regression (BLR)

In the studies of landslide susceptibility assessment which have been developed in recent years, statistical methods have increasingly been applied. Among all, the BLR (Binary Logistic Regression) certainly finds a more extensive application while MARS (Multivariate Adaptive Regression Splines), despite the good performance and the innovation of the strategies of analysis, only recently began to be employed as a statistical tool for predicting landslide occurrence. The purpose of this research was to evaluate the predictive performance and identify possible drawbacks of the two statistical techniques mentioned above, focusing in particular on the prediction of debris flows. To this aim, we employed an inventory of debris flows triggered by the passage of the hurricane IDA and the low-pressure system associated with it 96E, on November 7thand 8th2009 in the Caldera Ilopango, El Salvador (CA). Two validation strategies have been applied to both statistical techniques thus obtaining four models (BLR(I), MARS(I), BLR(II), MARS(II)) to be compared in pairs. Model performance was assessed in terms of AUC (area under the receiver operating characteristic (ROC) curve), Sensitivity, Specificity, Positive Prediction Value and Negative Prediction Value. Moreover, to evaluate the robustness of the modeling procedure, 50 replicates were created for each model and the standard deviation was calculated for each of them. The results show that both techniques allow for obtaining good or excellent performances so that it is not possible to define one of the two techniques as absolutely better. However, the validation procedure reveals slightly better performance of the MARS models, with greater sensitivity and greater discrimination among TNs

Mapping Susceptibility to Debris Flows Triggered by Tropical Storms: A Case Study of the San Vicente Volcano Area (El Salvador, CA)

In this study, an inventory of storm-triggered debris flows performed in the area of the San Vicente volcano (El Salvador, CA) was used to calibrate predictive models and prepare a landslide susceptibility map. The storm event struck the area in November 2009 as the result of the simultaneous action of low-pressure system 96E and Hurricane Ida. Multivariate Adaptive Regression Splines (MARS) was employed to model the relationships between a set of environmental variables and the locations of the debris flows. Validation of the models was performed by splitting 100 random samples of event and non-event 10 m pixels into training and test subsets. The validation results revealed an excellent (area under the receiver operating characteristic (ROC) curve (AUC) = 0.80) and stable (AUC std. dev. = 0.01) ability of MARS to predict the locations of the debris flows which occurred in the study area. However, when using the Youden’s index as probability threshold to discriminate between pixels predicted as positives and negatives, MARS exhibits a moderate ability to identify stable cells (specificity = 0.66). The final debris flow susceptibility map, which was prepared by averaging for each pixel the score of the 100 MARS repetitions, shows where future debris flows are more likely to occur, and thus may help in mitigating the risk associated with these landslides

Landslide Susceptibility Analysis on the Vicinity of Bogotá-Villavicencio Road (Eastern Cordillera of the Colombian Andes)

Landslide occurrence in Colombia is very frequent due to its geographical location in the Andean mountain range, with a very pronounced orography, a significant geological complexity and an outstanding climatic variability. More specifically, the study area around the Bogotá-Villavicencio road in the central sector of the Eastern Cordillera is one of the regions with the highest concentration of phenomena, which makes its study a priority. An inventory and detailed analysis of 2506 landslides has been carried out, in which five basic typologies have been differentiated: avalanches, debris flows, slides, earth flows and creeping areas. Debris avalanches and debris flows occur mainly in metamorphic materials (phyllites, schists and quartz-sandstones), areas with sparse vegetation, steep slopes and lower sections of hillslopes; meanwhile, slides, earth flows and creep occur in Cretaceous lutites, crop/grass lands, medium and low slopes and lower-middle sections of the hillslopes. Based on this analysis, landslide susceptibility models have been made for the different typologies and with different methods (matrix, discriminant analysis, random forest and neural networks) and input factors. The results are generally quite good, with average AUC-ROC values above 0.7–0.8, and the machine learning methods are the most appropriate, especially random forest, with a selected number of factors (between 6 and 8). The degree of fit (DF) usually shows relative errors lower than 5% and success higher than 90%. Finally, an integrated landslide susceptibility map (LSM) has been made for shallower and deeper types of movements. All the LSM show a clear zonation as a consequence of the geological control of the susceptibility